Ultrasound imaging simulators are used to train students to the technique of ultrasonography. However, the training offered using these types of devices is limited. Certain ultrasonographic diagnosis training apparatuses use 3-D arrays of ultrasonographic datasets based on the precise scanning of a real person's body with an ultrasound device or other medical diagnosis apparatuses, which is time-consuming and costly to obtain. The various ultrasonographic “slices” obtained during scanning are then saved in a database, and interpolation techniques are used to fill the gaps between slices. Most of the time the datasets are from healthy patients, or from patients with minor pathologies that do not require immediate treatment. It is very difficult to obtain ultrasonographic datasets with rare pathologies or disease conditions that require immediate intervention, as those patients are often not available for a prolonged time to do a complete scan of the region of interest or organ.
Furthermore, there is a finite amount of data in each case or dataset. The scanning windows and angles are limited in comparison to a real, clinical examination. The instructor and student may find themselves striving for an even better image, but that perfect image is often beyond the capabilities of the stored data.
Therefore, there is a need for improving ultrasonographic diagnosis training devices.